The invention relates to a diagnosis method and system for monitoring the available resources in a production process and to a diagnosis system with the aid of which this method can be implemented.
The production of complex products by a system provider takes place in a hierarchical production process in which a large number of different resources in the form of raw materials, semifinished products, components and services are required in the successive stages of production. These resources are procured by the system provider from supply links, some of which may be in-house suppliers, while others may be outside suppliers. To avoid capacity shortages in the supplies to the system provider, resources in the form of reserves and stocks are kept by the supply links, tying up a considerable proportion of capital. If these stocks become too great, the tied-up capital causes unnecessary costs. If the stocks become too low, on the other hand, delivery dates cannot be met. This is particularly so when there are fluctuations in demand. As a result, losses may arise. There is therefore a great need to optimize the available resources in the production process in such a way that the costs associated with them are minimized.
Conventional production planning and control systems deal with the questions and planning tasks arising during the design of the production process in a cascading procedure. This produces a static appraisal of the operations. Successful use of an integrated overall system for describing and planning the production process presupposes that all the data necessary for monitoring the production process can be made available at any time. This requires not only continuous monitoring of the reserves and stocks of all the supply links involved in the production process, but in particular also data concerning the design of the production and logistical processes, capacity utilization etc. of each individual supply link.
To obtain a realistic picture of the production process in its entirety and its behaviour when fluctuations in demand occur, the individual steps must be treated as parts of an integrated system which includes the complete production process. Such a planning and diagnosis system, with the aid of which a complex production process can be planned and constantly kept up-to-date for applications within a single company is known, for example, from WO 98/08177.
If, however, the production process also includes legally independent suppliers operating freely in the market, data which can be continuously called up concerning capacity utilization, production and logistical processes etc. of the supplier are generally not available. This information forms part of the core know-how of the supplier, which outside parties, in particular other suppliers or competitors—are not permitted to view. Consequently, existing overall systems for describing and planning the production process can be meaningfully used only for planning within a single company, and the systems will fail if they are distributed among different parties within different companies and if outside suppliers are incorporated.
Therefore, it is an object of the invention to propose a diagnosis method which permits continuous monitoring of the available resources in a production process in which outside supply links are incorporated. Furthermore, it is an object of the invention to provide a diagnosis system with which this diagnosis method can be implemented.
Accordingly, the entire network of supply links involved in the production process is replicated in its complexity, with the associated lead times for each individual supply link, in a diagnosis system. The diagnosis system also contains continuously updated data concerning the predicted gross demands and a demand forecast of the system provider, information on the current reserves and stocks of each individual supply link and, for each supply link, an identification number, which is a measure of the responsiveness of the supply link to changes in the demands of the system provider. The diagnosis system in this case replicates a production system operating on the “pull principle”, in which the demands of the system provider form the trigger for the entire production chain—and consequently also for each individual supply link.
The predicted demands of the system provider and the information concerning the current reserves and stocks of each supply link are used as a basis to calculate in the diagnosis system whether the current stocks of the supply link concerned are sufficient for the predicted demands of the system provider. The calculation uses the identification number of the supply link. The results of this calculation are available at any time to all the supply links —together with the structure and all the lead times of the entire network of supply links. Consequently, each supply link receives from the diagnosis system information on which amounts of the goods provided by it are required at which point in time by the system provider or by other supply links. On the other hand, the supply link learns at which points in the network capacity shortages have occurred and consequently has the possibility of adjusting its own capacities (stocks, capacity utilization etc.) accordingly. For example, if it can see in advance that another supply link, supplying to it, cannot provide the required amounts of raw material, the supply link can possibly look around in time for an alternative supplier. Or the supply link can establish that shortages exist in the case of another supply link, downstream in its supply chain, and this supply link will request lower sales volumes from the other supply link on the basis of the pull principle, and can cut back its own capacity in time. Each supply link can consequently detect shortages and help in advance to eliminate them. The supply link can also use this information to optimize its stocks, which for the most part result from inadequately coordinated capacities. Since stocks kept by supply links are synonymous with multiple storage of products at different value-adding stages, considerable savings can consequently be achieved in the entire production process.
By simultaneously providing all the information relevant to the system provider and the supply links in the diagnosis system, information flows both in the forward direction and in the backward direction are possible in the network of supply links. The diagnosis system consequently has the function of an early-warning system in the short-term and medium-term periods, allowing all those involved in the network to respond appropriately and timely to local disruptions in the production process. Furthermore, all changes in demand and stocks (for example in the stocks of a supply link) can be fed directly by the system provider and the supply links online into the diagnosis system and consequently can notify simultaneously all those involved in the production system. This allows phasing-out costs when a model is discontinued to be minimized. Furthermore the launch of a new model on the production system can take place in parallel with models already in production without great additional effort.
A particularly concise way of representing the supply capability of each supply link is achieved by using a traffic-light function, in which a supply link is given a “green light” if the stocks kept by this supply link correspond at least to the predicted demand, whereas the supply link is given a “red light” if its stocks are below the predicted demand.
To allow an uninterrupted information flow on the current standing of the supply chain to be ensured, even in the event of a data failure of a supplier, a lead time is expediently determined in advance for each supply link. The lead is the time interval between the incoming-goods or outgoing-goods point of this supply link and the assembly site of the system supplier. This is because, irrespective of the provision of data concerning current stocks by the supply links, it is possible on the basis of the demands of the system provider to calculate, using the lead time at any point in time the amounts of reserves, semifinished products etc. which should be present in the stores of the supply links at this point in time.
It is also expedient to use an interpreter list to reference the intermediates supplied by the supply links to the end product produced by the system provider. This interpreter list ensures the “translation” between the nomenclatures of parts of the supply links and the designation of parts used by the system provider, and ensures that each supply link is informed as to the amounts and types of raw materials and intermediates to be supplied by it, from which the end product is produced by the system provider.
The diagnosis system is expediently accessed via the Internet. In this way it can be ensured that supply links around the world can view the current status of the network at any time and can themselves feed their current data into the information system.
The diagnosis system consequently ensures the greatest possible transparency of the entire production process and the resources of all the supply links involved in it, since it is possible for outside supply links to obtain company-internal parameters for themselves at the same time. Although the supply link must specify an identification number, which is a measure of its supply capability (and consequently at least indirectly contains internal process and capacity utilization data), the determination of this identification number is left to each individual supply link itself. The supply link can consequently make known its supply capability and supply readiness by the choice of its identification number and at the same time retains the greatest possible autonomy.
A range, which is a measure of the time period over which the supply link is capable of balancing out fluctuations in demand of the system provider, is expediently chosen as the identification number of the supply link. If the supply link indicates a very small range for its supply capability, and consequently presents itself as very “agile”, it indicates by this that it can very rapidly adapt its process stage to changed demands of the system provider. However, this involves the risk of the supply link having supply problems if there are strong or medium-term fluctuations in the demand of the system provider, which is expressed by a “red” traffic light. If, on the other hand, the supply link indicates a very large range for its supply capability, this suggests that the supply link has large stocks, which it can use to balance out fluctuations in demand. Consequently, its traffic light remains “green” even when there are large changes in the demand of the system provider, but it must be assumed, in particular if ranges are exceedingly high, that the supply link has overdimensioned its store and is consequently keeping considerable dead capital.
Observing the traffic lights, and consequently monitoring the output of the diagnosis method, over a certain period of time therefore gives both the system provider and the supply links valuable indications as to whether, and to what extent, process stages and storage capacities of the supply links can be optimized, in order to ensure a satisfactory supply capability with the lowest possible storage costs. An important aspect here is that, in the diagnosis method according to the invention, the system provider primarily assumes the role of an observer and, in particular, need not assume any responsibility for the smooth operation of the supply chain. Only the structure of the supply network and continuously updated values for the predicted demand figures are provided to the system provider regulating the stocks kept by the supply links is then the responsibility of the supply links themselves. This is an important prerequisite for working together with legally independently operating companies. The diagnosis method consequently describes a self-regulating system in which the supply links choose, on the basis of information made available to them in the diagnosis system by the system provider and the other supply links, their own “optimum operating state” and consequently contribute to the optimization of the entire supply chain. In particular, no optimization of the entire supply network is carried out by the system provider. Such wide-ranging optimization would mean a far-reaching intervention into the autonomy of the supply links and would consequently be unacceptable to the majority of the supply links.
However, the diagnosis system allows the system provider to carry out continuous monitoring of shortages in stocks and in particular in supplies in the network of the supply links. Consequently, impending supply shortages among subcontracted suppliers can be detected in the short and medium terms. An early response to the shortages increases the delivery capability of the supply chain overall.